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Using Rough Sets to Improve Activity Recognition Based on Sensor Data

School of Fundamental Sciences, Massey University, Palmerston North 4442, New Zealand
This paper is an extended version of our paper published in Towards a Theory of Space for Activity Recognition in Smart Environments Based on Rough Sets, In Proceeing of the 11th International Conference on Intelligent Environments, Prague, Czech Republic, 15–17 July 2015.
Sensors 2020, 20(6), 1779; (registering DOI)
Received: 18 February 2020 / Revised: 19 March 2020 / Accepted: 19 March 2020 / Published: 23 March 2020
(This article belongs to the Section Intelligent Sensors)
Activity recognition plays a central role in many sensor-based applications, such as smart homes for instance. Given a stream of sensor data, the goal is to determine the activities that triggered the sensor data. This article shows how spatial information can be used to improve the process of recognizing activities in smart homes. The sensors that are used in smart homes are in most cases installed in fixed locations, which means that when a particular sensor is triggered, we know approximately where the activity takes place. However, since different sensors may be involved in different occurrences of the same type of activity, the set of sensors associated with a particular activity is not precisely defined. In this article, we use rough sets rather than standard sets to denote the sensors involved in an activity to model, which enables us to deal with this imprecision. Using publicly available data sets, we will demonstrate that rough sets can adequately capture useful information to assist with the activity recognition process. We will also show that rough sets lend themselves to creating Explainable Artificial Intelligence (XAI). View Full-Text
Keywords: activity recognition; context awareness; spatial reasoning; rough sets activity recognition; context awareness; spatial reasoning; rough sets
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Guesgen, H.W. Using Rough Sets to Improve Activity Recognition Based on Sensor Data. Sensors 2020, 20, 1779.

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